Factors impacting the detection of weed seed contaminants in seed lots

Abstract

BACKGROUND The setting and following of phytosanitary standards for weed seeds can lessen the impacts of weeds on agriculture. Standards adopted by seed companies, laboratories and regulators ensure the contamination rates do not exceed some thresholds. Globally sample size standards are set based on the amount needed to obtain a contaminant in a random sample of the seed lot, not detectability. New Zealand requires a 95% confidence that the maximum pest limit of 0.01% of quarantine weed seed contamination is not exceeded in an imported seed lot. We examined 24 samples each containing approximately 150 000 seeds of either perennial ryegrass (12 samples) or white clover seeds (12 samples) that were then spiked with seeds (contaminants) from 12 non‐crop species (3–8 seeds of each). We considered factors that may impact detection rates: shape, color, size, and texture relative to the crop, and technician (including a commercial seed laboratory). RESULTS A linear mixed model fitted to the data indicated significant observer, crop, and seed color, shape, and size effects on detection. Detectability increased by 20% ± 7.7 (± standard error) when seeds had a distinct shape or color (28% ± 8.1), or were larger (23% ± 8.7) rather than smaller, relative to the crop. Commercial laboratory identifications were usually correct at the level of genus, and species for common weeds, but some misidentifications occurred. CONCLUSION Sample sizes for border inspections should be based on detectability of regulated weed seeds in the crop in combination with weed risk for the crop and location. © 2022 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Efficient border biosecurity inspection leverages superspreading to reduce biological invasion risk

“…In our case study, we used $$R=1$$ for all pathways, knowing that we understand well how varying this parameter would affect our solution behavior and residual risk. 4.To account for the fact that inspection can be imperfect (Buddenhagen et al., 2023), we introduce an imperfect detectability parameter ($$d$$, unitless), which represents the likelihood of correctly identifying an infested unit as infested upon inspection. We thus replace the sample size in the propagule pressure equation (Equation ) with its effective sample size $$n \times d$$ (Trouve et al., 2022).…”

Assessing the invasive potential of different source populations of ragweed (Ambrosia artemisiifoliaL.) through genomically informed species distribution modelling

“…Our study provides key developments on this front: by incorporating genomic data into the established and commonly used ENM framework, we show how managers can identify the most concerning sources of an invasive species. This information will allow them to implement strategies like increasing the screening of goods coming from specific regions (Buddenhagen et al., 2023 ) or preventing imports from areas containing genetic variation of high concern.…”